Carteiras de Variância Mínima no Brasil
Rubesam, Alexandre, and Beltrame, Andre L. (2013), “Carteiras de Variância Mínima no Brasil”, Revista Brasileira de Financas, Vol. 11, No. 1. Available here.
Neste trabalho, investigamos carteiras de variância mínima no mercado de ações brasileiro, utilizando diferentes modelos de estimação da matriz de covariância, desde a simples matriz de covariância amostral até modelos GARCH multivariados. Comparamos os resultados das carteiras de variância mínima com os seguintes benchmarks: (i) o índice IBOVESPA, (ii) uma carteira igualmente ponderada, (iii) uma carteira formada através da maximização da razão de Sharpe e (iv) uma carteira formada através da maximização da média geométrica dos retornos. Os resultados mostram que as carteiras de variância mínima apresentam retornos maiores e volatilidades menores do que todos os benchmarks. Também avaliamos o desempenho de carteiras de variância mínima com alavancagem, do tipo 130/30, com resultados análogos. A carteira de variância mínima concentra os investimentos em um número pequeno de ações, com betas baixos em relação ao IBOVESPA, sendo facilmente replicáveis por investidores individuais ou institucionais.
Técnicas Quantitativas de Otimização de Carteiras Aplicadas ao Mercado de Ações Brasileiro
Santos, André A. P., and Tessari, Cristina (2012), “Técnicas Quantitativas de Otimização de Carteiras Aplicadas ao Mercado de Ações Brasileiro”, Revista Brasileira de Financas, Vol. 10, No 3. Available here.
Neste artigo examinamos a aplicabilidade e o desempenho fora da amostra das estratégias quantitativas de otimização por média-variância e mínima-variância com relação ao desempenho da carteira ingênua igualmente ponderada (1/N) e da carteira teórica do índice Ibovespa, bem como avaliamos a estabilidade das composições ótimas obtidas. Na obtenção de carteiras ótimas, restritas para venda a descoberto, foram empregadas matrizes de covariâncias estimadas com base em cinco abordagens alternativas: matriz de covariância amostral, matriz RiskMetrics, e três estimadores propostos por Ledoit & Wolf (2003, 2004a,b). Tomando como base diferentes frequências de rebalanceamento das carteiras, as medidas de desempenho fora da amostra indicam que as estratégias quantitativas de otimização proporcionam resultados estatisticamente signiﬁcativos em termos de menor volatilidade e desempenho ajustado ao risco superior. Além disso, o uso de estimadores mais soﬁsticados para a matriz de covariâncias gerou carteiras com menor turnover ao longo do tempo.
Low Risk Stocks Outperform within All Observable Markets of the World
Baker, Nardin L., and Haugen, R. A. (2012), “Low Risk Stocks Outperform within All Observable Markets of the World”, Available at SSRN.
This article provides global evidence supporting the Low Volatility Anomaly: that low risk stocks consistently provide higher returns than high risk stocks. This study covers 33 different markets during the time period from 1990-2011. (Two previous studies by Haugen & Heins (1972) and Haugen & Baker (1991) show the same negative payoff to risk in time periods 1926-1970 and 1970-1990.) The procedure for our study is intentionally simple, transparent and easily replicable. Our samples include non-survivors. We look at an international universe of stocks beginning with the first month of 1990 until December 2011; we compute the volatility of total return for each company in each country over the previous 24 months. Stocks in each country are ranked by volatility and formed into deciles. In the total universe and in each individual country low risk stocks outperform, the relationship with respect to Sharpe ratios is even more impressive. We believe this anomaly is caused primarily by agency issues, namely the compensation structures and internal stock selection processes at asset management firms which lead institutional investors on average to hold more volatile stocks. The article also addresses the implications for how corporate finance managers make capital investment decision in light of this evidence. The evidence presented here dethrones both CAPM and the Efficient Market Hypothesis.
The Volatility Effect in Emerging Markets
Blitz, David and Van Vliet, Pim (2012), “The Volatility Effect in Emerging Markets”, Available at SSRN.
We examine the empirical relation between risk and return in emerging equity markets and find that this relation is flat, or even negative. This is inconsistent with theoretical models such as the CAPM, which predict a positive relation, but consistent with the results of studies for developed equity markets. The volatility effect appears to be growing stronger over time, which we argue might be related to the increased delegated portfolio management in emerging markets. Finally, we find that the volatility effect in emerging markets is only weakly related to that in developed equity markets, which argues against a common-factor explanation.
The Limits to Arbitrage Revisited: The Low-Risk Anomaly
Li, X.; Sullivan, R. and García-Feijóo, L. (2012), “The Limits to Arbitrage Revisited: The Low-Risk Anomaly”, Available at SSRN.
We show that over a long study period (1963-2010), the efficacy of trading the well-known low-volatility stock anomaly more limited than widely believed. In particular, extracting excess returns associated with a zero-cost portfolio is meaningfully hampered by high transaction costs reflecting that the abnormal returns are concentrated among low liquidity stocks. Adding to the challenge, the anomalous excess returns quickly reverse requiring traders to rebalance frequently in attempting to extract profits, thus amplifying liquidity needs. Our findings are unchanged for various approaches to measuring the low-volatility anomaly.
The Low Volatility Effect: A Comprehensive Look
Soe, A. M. (2012), “The Low Volatility Effect: A Comprehensive Look, ”, Available at SSRN.
We analyze the low volatility effect in the U.S equity market with a focus on the common properties of various low volatility strategies. We examine the two major approaches to constructing low volatility portfolios and apply them to the U.S. equity market: mean-variance optimization-based versus the rankings or quantile-based approaches. Our analysis shows that both approaches are equally effective in reducing portfolio volatility over a long-term investment horizon. We then extend our analysis to the international and emerging markets. Our findings confirm that the low volatility effect is not unique to the U.S. equity markets; it is present on a global scale.
Betting Against Beta
Frazzine, A. and Pedersen, L. H. (2011), “Betting Against Beta”, Available at SSRN.
We present a model with leverage and margin constraints that vary across investors and time. We find evidence consistent with each of the model’s five central predictions: (1) Since constrained investors bid up high-beta assets, high beta is associated with low alpha, as we find empirically for U.S. equities, 20 international equity markets, Treasury bonds, corporate bonds, and futures; (2) A betting-against-beta (BAB) factor, which is long leveraged low beta assets and short high-beta assets, produces significant positive risk-adjusted returns; (3) When funding constraints tighten, the return of the BAB factor is low; (4) Increased funding liquidity risk compresses betas toward one; (5) More constrained investors hold riskier assets.
Um Índice de Mínima Variância de Ações Brasileiras
Thomé, César N.; Leal, Ricardo P. C. and Almeida, Vinício de S. (2011), “Um Índice de Mínima Variância de Ações Brasileiras”, Economia Aplicada, Vol 15. Available here.
Este trabalho desenvolve um índice de carteiras de mínima variância global (MVP) para as ações mais líquidas do Brasil. Os resultados indicaram que a MVP sem limites sobre os pesos das ações não apresenta diferença significativa de desempenho em relação ao IBOVESPA. A imposição de um peso máximo de dez por cento em cada ação tornou possível superar o IBOVESPA. Contudo, os resultados desta estratégia são comparáveis aos de uma carteira igualmente ponderada e são superados por alguns fundos de gestão ativa em testes fora da amostra. Ainda assim, estas estratégias simples baseadas nestas restrições facilitam a replicação do MVP por investidores individuais e exchange traded funds e sustenta o poder de estratégias ingênuas de investimento.
Benchmarking as Limits to Arbitrage: Understanding the Low-Volatility Anomaly
Baker, M., Bradley, B. and Wurgler, J. (2011), “Benchmarking as Limits to Arbitrage: Understanding the Low-Volatility Anomaly”, Financial Analyst’s Journal, Vol. 67. Available here.
Contrary to basic finance principles, high-beta and high-volatility stocks have long underperformed low-beta and low-volatility stocks. This anomaly may be partly explained by the fact that the typical institutional investor’s mandate to beat a fixed benchmark discourages arbitrage activity in both high-alpha, low-beta stocks and low-alpha, high-beta stocks.
Risk and Return in General: Theory and Evidence
Falkenstein, Eric G.(2009), “Risk and Return in General: Theory and Evidence”, Journal of Portfolio Management, pp. 102-113, Fall 2007. Available at SSRN.
Empirically, standard, intuitive measures of risk like volatility and beta do not generate a positive correlation with average returns in most asset classes. It is possible that risk, however defined, is not positively related to return as an equilibrium in asset markets. This paper presents a survey of data across 20 different asset classes, and presents a model highlighting the assumptions consistent with no risk premium. The key is that when agents are concerned about relative wealth, risk taking is then deviating from the consensus or market portfolio. In this environment, all risk becomes like idiosyncratic risk in the standard model, avoidable so unpriced.
The Volatility Effect: Lower Risk Without Lower Return
Blitz, David and Van Vliet, Pim (2007), “The Volatility Effect: Lower Risk Without Lower Return”, Journal of Portfolio Management, pp. 102-113, Fall 2007. Available at SSRN.
We present empirical evidence that stocks with low volatility earn high risk-adjusted returns. The annual alpha spread of global low versus high volatility decile portfolios amounts to 12% over the 1986-2006 period. We also observe this volatility effect within the US, European and Japanese markets in isolation. Furthermore, we find that the volatility effect cannot be explained by other well-known effects such as value and size. Our results indicate that equity investors overpay for risky stocks. Possible explanations for this phenomenon include (i) leverage restrictions, (ii) inefficient two-step investment processes, and (iii) behavioral biases of private investors. In order to exploit the volatility effect in practice we argue that investors should include low risk stocks as a separate asset class in the strategic asset allocation phase of their investment process.
The Cross-Section of Volatility and Expected Returns
Ang, Andrew; Hodrick, Robert J.; Xing, Yuhang and Zhang, Xiaoyan (2006), “The Cross-Section of Volatility and Expected Returns”, Journal of Finance, 61(1), 259-299. Available at SSRN.
We examine how volatility risk, both at the aggregate market and individual stock level, is priced in the cross-section of expected stock returns. Stocks that have past high sensitivities to innovations in aggregate volatility have low average returns. We also find that stocks with past high idiosyncratic volatility have abysmally low returns, but this cannot be explained by exposure to aggregate volatility risk. The low returns earned by stocks with high exposure to systematic volatility risk and the low returns of stocks with high idiosyncratic volatility cannot be explained by the standard size, book-to-market, or momentum effects, and are not subsumed by liquidity or volume effects.
Minimum-Variance Portfolios in the US Equity Market
Clarke, Roger; Harindra de Silva and Throley, Steven (2006), “Minimum-Variance Portfolios in the US Equity Market”, Journal of Portfolio Management,10-24, 2006. Available at here.